Seismic microzonation and building vulnerability assessment based on site characteristic and geotechnical parameters by use of Fuzzy-AHP model (a case study for Kermanshah city)

ABSTRACT

On 12th November 2017, an earthquake with a magnitude of 7.3 Richter scale in the town of Sarpol-e Zahab took place that caused lots of human casualties and devastation. After the incident, issues related to the probability of an earthquake with equal intensity and extents of similar building destruction were raised in Kermanshah city. Therefore, a seismic microzonation map of Kermanshah city has been prepared based on the geotechnical, geological, and geophysical data, and the data were analysed using Fuzzy analytic hierarchy process (AHP). In the next step, the vulnerability analysis of city buildings was carried out based on the ground-shaking map, vulnerability curves, and statistical data regarding the buildings. The results of the vulnerability rate of residential buildings indicate that 80% of residential buildings would be exposed to vulnerability from low to moderate. However, other buildings would suffer 2% fully destruction (D1), 7% very high destruction (D2) and 11% high destruction (D3), respectively. Finally, according to the obtained results, the proposed model is verified with the help of the data and observations from the Sarpol-e Zahab earthquake, which reveals that the model is in good agreement with the actual earthquake data.     

Comparing the Effects of Microwave and Conventional Heating Methods on the Evaporation Rate and Quality Attributes of Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L.) Juice Concentrate

Pomegranate juice was concentrated by conventional heating and microwave heating at different operational pressures (12, 38.5, and 100 kPa), and their effects on evaporation rate and quality attributes of concentrated juice were investigated. The final juice concentration of 40° Brix was achieved in 140, 127, and 109 min at 100, 38.5, and 12 kPa, respectively, by using conventional heating. Applying microwave energy decreased required times to 118, 95, and 75 min. The changes in color, anthocyanin content, and antioxidant capacity during concentration processes were investigated. L*, a*, and b* parameters were measured to estimate the intensity of color loss. All Hunter color parameters decreased with time. Results showed that the degradation of color, anthocyanins, and antioxidant activity were more important in conventional heating compared to microwave heating method. Degradation rates increases by increasing process pressure. A first-order kinetics model was applied to modeling changes in total solid content, anthocyanin content, and antioxidant capacity.     

Incorporating microgrids coupling with utilization of flexible switching to enhance self-healing ability of electric distribution systems

Electric distribution networks have to deal with issues caused by natural disasters. These problems possess unique characteristics, and their severity can make load restoration methods impotent. One solution that can help in alleviating the aftermath is the use of microgrids (MGs). Employing the cumulative capacity of the generation resources through MG coupling facilitates the self-healing capability and leads to better-coordinated energy management during the restoration period, while the switching capability of the system should also be considered. In this paper, to form and schedule dynamic MGs in distribution systems, a novel model based on mixed-integer linear programming (MILP) is proposed. This approach employs graph-related theories to formulate the optimal formation of the networked MGs and management of their proper participation in the load recovery process. In addition, the Benders decomposition technique is applied to alleviate computability issues of the optimization problem. The validity and applicability of the proposed model are evaluated by several simulation studies.     

COVID-19 pandemic in the Middle East countries: coronavirus-seeking behavior versus coronavirus-related publications

Scientometrics - The spread of COVID-19 has created a fundamental need for coordinated mechanisms responding to outbreaks in different sectors. One of the main sectors relates to information supply...     

Investigation of the Effect of Foaming Agent on the Fabrication of NiTi Foams Using the Self-Propagating,High-Temperature Synthesis Process

Metallurgical and Materials Transactions B - Fabrication of NiTi intermetallic foams using the self-propagating, high-temperature synthesis (SHS) process was investigated. Weighed amounts of...     

An improved Differential Evolution algorithm using learning automata and population topologies

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable strategy for the construction of a mutant vector for each individual, adaptively. The proposed automata based DE is able to maintain the diversity among the individuals and encourage them to move toward several promising areas of the search space as well as the best found position. Numerical experiments are conducted on a set of twenty four well-known benchmark functions and one real-world engineering problem. The performance comparison between ADE-Grid and other state-of-the-art DE variants indicates that ADE-Grid is a viable approach for optimization. The results also show that the proposed ADE-Grid improves the performance of DE in terms of both convergence speed and quality of final solution.     

Using the genetic algorithm to enhance nonnegative matrix factorization initialization

Nonnegative matrix factorization (NMF) algorithms have been utilized in a wide range of real applications; however, the performance of NMF is highly dependent on three factors including: (1) choosing a problem dependent cost function; (2) using an effective initialization method to start the updating procedure from a near‐optimal point; and (3) determining the rank of factorized matrices prior to decomposition. Due to the nonconvex nature of the NMF cost function, finding an analytical‐based optimal solution is impossible. This paper is aimed at proposing an efficient initialization method to modify the NMF performance. To widely explore the search space for initializing the factorized matrices in NMF, the island genetic algorithm (IGA) is employed as a diverse multiagent search scheme. To adapt IGA for NMF initialization, we present a specific mutation operator. To assess how the proposed IGA initialization method efficiently enhances NMF performance, we have implemented state‐of‐the‐art initialization methods and applied to the Japanese Female Facial Expression dataset to recognize the facial expression states. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of relative error and fast convergence.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.